docstr: Create logical separations between "columns" of data for a :class:`BaseDataset`
  
  Parameters
  ----------
  d: pd.DataFrame, or None
      Raw data representing the initial state of the data to be handled by this chunk, and its
      transformed self (:attr:`BaseDataChunk.T`)
  
  Attributes
  ----------
  T: _BaseDataChunk
      Extra data chunk tracking transformations/inversions applied to :class:`_BaseDataChunk`
      attributes via :class:`~hyperparameter_hunter.feature_engineering.FeatureEngineer`. If
      no feature engineering is performed, `T` can be ignored
docstr_tok: ["Create","logical","separations","between","``","columns","''","of","data","for","a",":","class",":","`","BaseDataset","`","Parameters","--","--","--","--","--","d",":","pd.DataFrame",",","or","None","Raw","data","representing","the","initial","state","of","the","data","to","be","handled","by","this","chunk",",","and","its","transformed","self","(",":","attr",":","`","BaseDataChunk.T","`",")","Attributes","--","--","--","--","--","T",":","_BaseDataChunk","Extra","data","chunk","tracking","transformations\/inversions","applied","to",":","class",":","`","_BaseDataChunk","`","attributes","via",":","class",":","`","~hyperparameter_hunter.feature_engineering.FeatureEngineer","`",".","If","no","feature","engineering","is","performed",",","`","T","`","can","be","ignored"]
code: def __init__(self, d: OptionalDF):
      """Create logical separations between "columns" of data for a :class:`BaseDataset`
  
      Parameters
      ----------
      d: pd.DataFrame, or None
          Raw data representing the initial state of the data to be handled by this chunk, and its
          transformed self (:attr:`BaseDataChunk.T`)
  
      Attributes
      ----------
      T: _BaseDataChunk
          Extra data chunk tracking transformations/inversions applied to :class:`_BaseDataChunk`
          attributes via :class:`~hyperparameter_hunter.feature_engineering.FeatureEngineer`. If
          no feature engineering is performed, `T` can be ignored"""
      super().__init__(d=d)
      self.T: _BaseDataChunk = _BaseDataChunk(d=d)
  
imports: import pandas
  from typing import List, Optional
label_desc: Use image node_id 1 to create a new BaseDataChunk object from inherited base classes: _BaseDataChunk with example: obj = BaseDataChunk(d)